A Method for Generating Optimal Attack Strategies for Wind Farms Considering DoS and FDIA

Abstract

This application relates to a method for generating optimal attack strategies for wind farms considering DoS and FDIA attacks. The method includes: first, establishing an optimized control model and an optimal scheduling model for normal operation of the wind turbine; then, constructing an FDIA control command tampering attack model, a DoS attack model, and an FDIA wind speed measurement tampering attack model, respectively; and finally, combining wind turbine operation constraints and attack resource constraints to solve for the attack strategy that maximizes the total power output loss of the wind farm and complete a quantitative assessment of the attack impact. This invention integrates DoS and FDIA attacks into the physical operation constraint framework of the wind turbine, generating an implementable optimal attack strategy with the goal of maximizing power output loss, achieving unified quantitative assessment of multiple attack scenarios, and providing support for the network security risk analysis and protection of wind farms.

Description

Technical Field

[0001] This application relates to the field of power system network security and cyber-physical system technology, and in particular to a method for generating optimal attack strategies for wind farms that takes into account DoS and FDIA. Background Technology

[0002] With the high proportion of new energy sources connected to the grid, wind farms are gradually exhibiting typical characteristics of deep cyber-physical coupling: the control layer consists of the wind turbine main control system, converter controller, active / reactive power regulation module, wind turbine status monitoring, and wind speed measurement device, realizing wind turbine power scheduling, pitch adjustment, and grid connection stability control; at the same time, it relies on information communication links and data acquisition and monitoring control systems to complete measurement transmission and control distribution. Under this architecture, once communication availability or data integrity is compromised, the system may experience a chain reaction of disconnection—loss of control—instability, which may lead to fluctuations in wind power output and even risks to grid connection stability.

[0003] In wind farm scenarios, Denial-of-Service (DoS) attacks and False Data Injection (FDIA) attacks are two typical threats. Attackers may infiltrate the office network and laterally penetrate the wind farm's local area network via phishing emails and Trojans, hijacking the wind turbine main control and converter controllers, tampering with speed / pitch angle / torque commands, and concurrently manipulating multiple units, causing a significant drop in wind farm output. Meanwhile, DoS attacks can lead to control command freezes, communication delays, or data loss, while FDIA attacks can forge wind speed and other measurements to mislead scheduling optimization and create pseudo-optimal decisions.

[0004] For DoS / FDIA and their coordinated attacks, existing technologies have proposed various modeling and optimization methods. Most existing solutions construct attack combinations based on grid topology and measurement data, and perform combinatorial optimization searches with objectives such as attack cost. These methods are more inclined towards coordinated attack modeling at the state estimation / measurement set level. When dealing with the closed-loop control-aerodynamic-power curves of multiple wind farm units, they often struggle to simultaneously constrain wind turbine control variables and operating boundaries, making it difficult to directly output an implementable optimal strategy aimed at minimizing the total wind farm output. Some existing technologies propose a coordinated modeling and strategy optimization approach for FDIA and low-rate DoS. These solutions emphasize general strategy optimization and detection avoidance of coordinated attacks. However, when targeting wind farms, they do not explicitly focus the attack target on minimizing the total wind farm power / maximizing total output loss, nor are they strongly coupled with wind turbine control variables and their physical feasible domains, failing to generate an optimal attack strategy that is both implementable and easily comparable for evaluation.

[0005] Therefore, there is an urgent need in related technologies to integrate DoS and FDIA into the wind turbine operation constraints and control closed loop under attack resource constraints, and to generate the optimal attack strategy with the goal of minimizing the total output of the wind farm. Summary of the Invention

[0006] Therefore, it is necessary to provide a method for generating the optimal attack strategy for wind farms that considers both DoS and FDIA to address the aforementioned technical problems.

[0007] Firstly, this application provides a method for generating an optimal attack strategy for wind farms that considers both DoS and FDIA. The method includes: Establish an optimized control model and an optimal scheduling model for normal operation of wind turbine units; FDIA control command tampering attack model, DoS attack model and FDIA wind speed measurement tampering attack model were constructed respectively. Combining wind turbine operation constraints and attack resource constraints, the system solves for the attack strategy that maximizes the total power output loss of the wind farm based on the constructed model, and completes a quantitative assessment of the attack impact.

[0008] Optionally, in one embodiment of this application, the wind turbine optimization control model includes a wind turbine power output model, a power coefficient model, a tip speed ratio model, and constraints on wind speed, rotor speed, pitch angle, power coefficient, and tip speed ratio.

[0009] Optionally, in one embodiment of this application, the normal operation optimal scheduling model takes maximizing the total power of the wind farm as the optimization objective, solves for the optimal control parameters, and calculates the optimal power output.

[0010] Optionally, in one embodiment of this application, the FDIA control command tampering attack model sets tampering constraints on the wind turbine angular velocity and pitch angle, and substitutes the tampered parameters into the power model to calculate the power output after the attack.

[0011] Optionally, in one embodiment of this application, the DoS attack model freezes the wind turbine control variables to their values ​​before the attack and calculates the actual power output after the attack by combining them with the real-time wind speed.

[0012] Optionally, in one embodiment of this application, the FDIA wind speed measurement tampering attack model injects forged wind speed data, solves for pseudo-optimal scheduling parameters based on the forged wind speed data, and calculates the actual power output in combination with the real wind speed.

[0013] Optionally, in one embodiment of this application, the quantitative assessment of the attack impact is based on normal optimized operation, and the deviations of the indicators such as power output, rotational speed, tip speed ratio, and pitch angle are compared and calculated.

[0014] Secondly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the steps of the methods described in the various embodiments above.

[0015] Thirdly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the methods described in the various embodiments above.

[0016] The above-mentioned method for generating optimal attack strategies for wind farms, considering both DoS and FDIA, has the following advantages compared to existing technologies: First, for the closed loop of "control, measurement, and output" of wind farms, three types of attack models are established: DoS control freezing, FDIA control signal tampering, and FDIA wind speed measurement forgery. Attack strategies are generated under the same framework, which can cover the typical attack chain of wind farms and solve the problem that existing technologies are mostly single attacks or only measure layer analysis, and it is difficult to characterize the synergistic effects.

[0017] Second, the wind turbine operating boundaries (such as wind speed, rotor speed, blade pitch angle, tip speed ratio, etc.) are introduced into the strategy generation process, and a reasonable range of attack disturbances is set, so that the generated attack strategy is more in line with the wind turbine's operating range, is feasible, and is not prone to significant anomalies, thus solving the problem that existing technologies ignore physical constraints, making it difficult to implement the strategy.

[0018] Third, using normal optimal operation as a benchmark, the system solves the strategy that maximizes the total power output loss of the wind farm under limited attack resources. It also conducts a unified comparative evaluation of normal, DoS, and FDIA (control / measurement) scenarios, and can output reproducible "worst-case" attack samples, providing a basis for wind farm risk assessment and protection reinforcement. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a method for generating the optimal attack strategy for a wind farm that considers DoS and FDIA in one embodiment. Figure 2 This is a diagram of a wind power control command tampering attack chain in one embodiment; Figure 3 This is a diagram of a wind power DoS attack chain in one embodiment; Figure 4 This is a diagram of a wind speed measurement tampering attack chain in one embodiment; Figure 5 This is a schematic diagram illustrating the power output variation characteristics under four scenarios in one embodiment; Figure 6 This is a schematic diagram illustrating the rotational speed variation characteristics under four scenarios in one embodiment; Figure 7 This is a schematic diagram illustrating the characteristics of tip speed ratio variation under four scenarios in one embodiment; Figure 8 This is a schematic diagram illustrating the pitch angle variation characteristics under four scenarios in one embodiment; Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0021] In one embodiment, such as Figure 1 As shown, a method for generating the optimal attack strategy for wind farms considering DoS and FDIA is provided, including the following steps: S101: Establish the optimal control model and the optimal scheduling model for normal operation of wind turbine units.

[0022] In this embodiment, firstly, a wind turbine power output model is established to provide a benchmark for subsequent attack impact assessment. Empirical fitting parameters are obtained by curve fitting the aerodynamic characteristic curve of the wind turbine, and a power coefficient model is established. A tip speed ratio model is then established. Constraints such as wind speed constraint, rotor speed constraint, blade pitch angle constraint, power coefficient constraint, and tip speed ratio constraint are also established.

[0023] In one embodiment of this application, the output power of the fan is expressed as:

[0024] in, Represents air density; Represents the swept area; Represents the power factor; Represents the tip speed ratio; Represents the pitch angle; Represents wind speed.

[0025] In one embodiment of this application, wind speed range needs to be carefully considered when designing a wind turbine, as wind speed is a key factor directly affecting the turbine's power output. Even small changes in wind speed can lead to significant changes in power output. Rated wind speed refers to the wind speed at which the turbine will produce its maximum rated power output. Below or above the rated wind speed, the power output of the wind turbine will vary with the wind speed. When the wind speed exceeds the cut-out wind speed, the turbine will stop operating normally to ensure its safety and prevent damage. This cut-out protection mechanism prevents the turbine from being overloaded and damaged under strong wind conditions. Conversely, when the wind speed is below the cut-in wind speed, the turbine will not be able to generate sufficient power to meet requirements, and will therefore stop generating electricity and may automatically shut down to avoid equipment damage. Careful consideration of the wind speed range is crucial for the design and operation of wind turbines. By correctly setting the cut-in and cut-out wind speeds, it is ensured that the turbine operates within a safe range and its power generation efficiency is maximized. Simultaneously, monitoring and adjusting wind speed changes within the range allows the turbine to operate stably under different meteorological conditions, improving its reliability and stability. Therefore, when designing and operating wind turbines, the wind speed range and its impact on turbine performance and safety must be fully considered to ensure stable and efficient operation. Wind speed The range can be expressed as:

[0026] in, , These represent the cut-in wind speed and the cut-out wind speed, respectively.

[0027] In one embodiment of this application, the rotor speed of the wind turbine is a key parameter that directly affects its power output, controlled by adjusting the rotational speed of the turbine blades. In an automatic power generation regulation system, if the wind speed is too high, the rotor speed may need to be reduced to prevent overload and blade damage. Conversely, if the wind speed is too low, the rotor speed may need to be increased to maintain a constant tip speed ratio and ensure the wind turbine operates at optimal efficiency. Rotor speed is a critical parameter that must be carefully controlled under different wind speed conditions to ensure efficient and safe operation of the wind turbine. In an automatic power generation regulation system, the rotor speed can be automatically adjusted by monitoring real-time wind speed and the wind turbine's operating status to optimize power generation efficiency and stability. By precisely controlling the rotor speed, the automatic power generation regulation system enables the wind turbine to maintain stable power generation output under various wind speed conditions and maximize the utilization of available wind resources, thereby improving the wind turbine's power generation efficiency and economy. In addition to real-time monitoring and regulation in the automatic power generation regulation system, the wind turbine's operation can also be controlled by a pre-set rotor speed range. This ensures that the wind turbine can operate stably and achieve optimal power generation efficiency under different meteorological conditions. Therefore, precise control of rotor speed is crucial for the efficient operation of wind turbines. It not only improves power generation efficiency but also extends the turbine's lifespan and reduces maintenance costs. The rotor speed range is set as follows:

[0028] in, Representing the The rotor speed of the typhoon generator; and Representing the first Minimum and maximum rotor speeds within the operating range of the typhoon generator.

[0029] In one embodiment of this application, the blade pitch angle of a wind turbine is a key parameter that affects its power output by determining the amount of wind energy captured and converted into mechanical energy by the turbine. By adjusting the blade pitch angle, the wind turbine can achieve optimal operating efficiency and safety under different wind speed conditions. At higher wind speeds, increasing the blade pitch angle reduces the stress on the turbine and prevents it from rotating too quickly, thus protecting it from overload and damage. This adjustment helps ensure stable operation of the turbine under strong wind conditions and extends its service life. Conversely, at lower wind speeds, decreasing the blade pitch angle increases the angle of attack of the blades, thereby improving the turbine's ability to capture wind energy and maximizing the utilization of available wind resources. This adjustment helps improve the turbine's power generation efficiency under low wind speed conditions, thereby increasing the overall power generation of the entire wind farm. Therefore, the blade pitch angle is an important tool for optimizing wind turbine efficiency and safety, playing a crucial role in maximizing the energy generated from a given wind resource. Proper adjustment of the blade pitch angle is one of the key factors in optimizing wind turbine efficiency and safety. By precisely controlling the pitch angle, wind turbines can operate stably under various wind speed conditions and maximize their power generation efficiency, thereby achieving efficient utilization of wind energy resources. Pitch angle The range is set as follows:

[0030] in, and Representing the first Minimum and maximum pitch angles within the operating range of the typhoon.

[0031] In one embodiment of this application, the power coefficient of a wind turbine is a key parameter, representing the efficiency of converting wind energy into mechanical energy. The power coefficient is influenced by various factors, including turbine blade design, wind speed, and blade angle. At different blade angles, the power coefficient exhibits different curves, demonstrating that blade design and adjustment significantly impact wind turbine performance. For a given wind turbine design, the power coefficient exhibits different characteristics at different blade angles. The maximum power coefficient is limited by the Betz limit, a theoretical upper limit of approximately 59.3%. This means that the wind turbine cannot exceed this limit to extract energy from the wind. Furthermore, a high power coefficient can lead to overload of wind turbine components, resulting in mechanical failure. Conversely, the minimum power coefficient is typically limited by the wind turbine's design and its operating conditions. A low power coefficient means the wind turbine is not effectively extracting energy from the wind, which can lead to reduced power output and efficiency. Therefore, the power coefficient of a wind turbine should be adjusted to achieve a balance between maximum power output and ensuring turbine lifespan and safety. The power coefficient of a wind turbine can be set within a specific range to ensure stable operation under various wind speed conditions and maximize the utilization of wind energy resources. The power coefficient range for a wind turbine can be set as follows:

[0032] in, Representing the The power factor of a typhoon generator; and and These represent the minimum and maximum power coefficients of the wind turbine, respectively.

[0033] It can be explicitly represented as:

[0034] in, , , , , The parameters are empirical fitting parameters, obtained by curve fitting the aerodynamic characteristic curve of the fan. This is the overall scaling factor, controlling... The height of the peak value; Determines the tip speed ratio position near the peak; and Describe the trend that "the larger the pitch angle, the smaller the power coefficient"; This is the exponential decay coefficient.

[0035] The tip speed ratio can be explicitly expressed as:

[0036] in, This represents the linear translation effect of the pitch angle on the "effective tip speed ratio". The larger the pitch angle, the smaller the effective tip speed ratio. This is a correction factor.

[0037] In one embodiment of this application, the tip speed ratio is a key parameter in the wind energy field, used to characterize the performance of wind turbines because it significantly affects the turbine's power coefficient. Generally, a higher tip speed ratio indicates that the blade tip moves faster relative to the wind, which can lead to increased power output. A high tip speed ratio means that the blade moves more air mass per unit time, thereby capturing more wind energy. However, while a high tip speed ratio can improve power output, it is not suitable in all situations. Every wind turbine design has an optimal tip speed ratio above which the turbine's efficiency decreases. Therefore, adjusting the tip speed ratio to suit specific wind energy conditions is crucial. Conversely, if the tip speed ratio is too low, the wind turbine will not be able to capture wind energy efficiently, resulting in reduced power output. At a low tip speed ratio, the blade rotation speed may not be sufficient to fully utilize the kinetic energy of the wind, leading to reduced turbine efficiency. Therefore, wind turbines need to optimize power output by adjusting the tip speed ratio while avoiding excessive stress on the turbine. Thus, adjusting the tip speed ratio is one of the important means of optimizing wind turbine performance. By ensuring the tip speed ratio is within an appropriate range, efficient turbine operation can be achieved, maximizing the utilization of wind energy resources while extending the turbine's lifespan. Therefore, the tip speed ratio must be carefully considered during the design and operation of wind turbines to ensure their stability and efficiency under various operating conditions. The range of the tip speed ratio can be written as:

[0038] in, Represents the first The tip speed ratio of a typhoon turbine blade; and These represent the minimum and maximum tip speed ratios of the fan, respectively. Representing the The rotor angular velocity of the typhoon; Represents the blade radius.

[0039] In one embodiment of this application, the normal operation optimal scheduling model takes maximizing the total power of the wind farm as the optimization objective, solves for the optimal control parameters, and calculates the optimal power output.

[0040] In one embodiment of this application, an optimization objective function is established. Under normal operating conditions, the optimizer solves for the optimal control parameters based on the current wind speed to maximize the total power of the wind farm. Substituting the optimal control parameters into the power model, the optimal power of each wind turbine is obtained, and the summation yields the total optimal power of the wind farm. The formula is expressed as follows:

[0041]

[0042] in, and The optimized angular velocity and propeller pitch angle; Representing the The wind speed of the typhoon at time t2; This represents the optimized unit power.

[0043] S102: Construct FDIA control command tampering attack model, DoS attack model and FDIA wind speed measurement tampering attack model respectively.

[0044] In one embodiment of this application, an FDIA control command tampering attack model is established. The control command tampering attack occurs at the level of the wind turbine main control system and the converter controller, such as... Figure 2 As shown, attackers typically infiltrate wind farm office networks or maintenance laptops via phishing emails, Trojan programs, and malicious USB drives. They then use lateral movement techniques to penetrate the wind farm's local area network. By cracking weak passwords or using unencrypted Modbus / TCP channels, attackers can directly modify the turbine's torque and pitch angle commands. This type of attack is highly stealthy; attackers often adjust only a few control variables, deviating them from normal boundaries without triggering protection mechanisms, leading to abnormal turbine output or even damage to mechanical components. Ultimately, this can cause drastic fluctuations in wind farm output, affecting the system's grid connection stability.

[0045] First, establish the angular velocity manipulation model:

[0046] in, The angular velocity after the attack; and for Upper and lower limit constraints.

[0047] The angular velocity should not deviate too much from that during normal operation.

[0048] in, This is the angular velocity during normal operation. This represents the maximum deviation between the angular velocity after the attack and the angular velocity during normal operation, ensuring the stealth of the attack.

[0049] Next, a pitch angle tampering model was established:

[0050] in, The propeller pitch angle after the attack; and for Upper and lower limit constraints.

[0051] The pitch angle should not deviate too much from that during normal operation:

[0052] in, This is the pitch angle during normal operation. This represents the maximum deviation between the pitch angle after the attack and the pitch angle during normal operation.

[0053] Finally, the power output after the attack is calculated, and the tampered parameters are substituted into the power model to obtain the actual power.

[0054] In one embodiment of this application, a DoS attack model is established, where the DoS attack occurs on the communication link between the wind turbine controller and the site's SCADA system, such as... Figure 3 As shown, attackers continuously send high-volume invalid requests to the wind turbine's communication port by forging legitimate addresses, exhausting communication stack resources and preventing the turbine from receiving new scheduling commands. Control variables are forced to remain at their pre-attack values. If wind speed changes rapidly during the attack, the turbine will operate under incorrect control settings, potentially leading to overload, inefficiency, or dangerous operating conditions. Large-scale concurrent DoS attacks can even cause the entire wind farm's control to fail, resulting in unstable power output from the wind turbine cluster.

[0055] First, establish a control variable freezing model: at time... During a DoS attack, the wind turbine cannot receive new scheduling commands, and its angular velocity and pitch angle remain at the values ​​of the previous moment.

[0056] in, for angular velocity at time t; for The pitch angle at any given moment.

[0057] Next, the actual power output of the wind turbines under real wind speeds after the attack is calculated:

[0058] in, This is a function for calculating the turbine power of the unit. for The wind speed at any given moment.

[0059] Finally, by adding up the power of each wind turbine, the total power of the wind farm is calculated as follows:

[0060] in, This represents the total power output of the wind farm under a DoS attack.

[0061] In one embodiment of this application, an FDIA wind speed measurement tampering attack model is established. Wind speed measurement tampering attacks are a typical type of spoofed data injection attack. Attackers can implant malicious firmware in the wind speed sensor supply chain or perform man-in-the-middle injection to forge wind speeds in the wireless wind measurement link, such as... Figure 4 As shown, the dispatch center calculated a "pseudo-optimal" output curve based on tampered wind speeds, resulting in a significant discrepancy between power commands and actual operating conditions. When a large number of units operate simultaneously under erroneous wind speed estimates, widespread output deviations, tracking failures, or power fluctuations may occur, thereby affecting the stability of the regional power grid.

[0062] First, create a fake wind speed model:

[0063] in, The falsified wind speed of the fan. The wind speed deviation was falsified.

[0064] Then, the optimizer solves for pseudo-optimal scheduling parameters based on the fake wind speed:

[0065] in, This is a pseudo-optimal angular velocity. This is a pseudo-optimal propeller pitch angle.

[0066] Next, the actual power after the attack is calculated.

[0067] Because the wind turbine is actually operating at the true wind speed The system operates within a certain range, but uses control parameters calculated based on falsified wind speeds; therefore, the actual power is:

[0068] in, This represents the unit power under an FDI attack.

[0069] Finally, the power of each wind turbine is added together to calculate the total power of the wind farm:

[0070] in, This represents the total power output of the wind farm under FDI attack.

[0071] S103: Combining wind turbine operation constraints and attack resource constraints, based on the constructed model, solve the attack strategy that maximizes the total power output loss of the wind farm, and complete the quantitative assessment of the attack impact.

[0072] In this embodiment, combining wind turbine operation constraints and attack resource constraints (number of attacked turbines, duration, and tampering extent), the optimal attack combination strategy is solved to maximize the total power loss of the wind farm, resulting in the worst-case attack scheme. Four evaluation scenarios are defined: Scenario 1: Normal optimized operation; Scenario 2: DoS attack; Scenario 3: FDIA control signal tampering; Scenario 4: FDIA measurement signal tampering. Deviations in key indicators are calculated: the deviations in power output, speed, tip speed ratio, and pitch angle are compared to quantify the impact of the attack on power output.

[0073] like Figure 5 , Figure 6 , Figure 7 , Figure 8 As shown, the key optimization indicators of 10 wind turbine units in the wind farm under four scenarios—normal baseline optimization, DoS attack (control freeze), FDIA—control signal tampering, and FDIA—measurement signal tampering—are shown, including the variation characteristics of power output, speed, tip speed ratio, and pitch angle.

[0074] First, from the perspective of power output, under normal optimization scenarios, the power output of each unit is relatively stable, exhibiting reasonable differences consistent with wind speed distribution and wake models, and is generally at the upper-middle level of the optimal feasible region. In contrast, DoS attacks prevent control commands from being updated, causing the units to maintain the control values ​​from the previous moment. This results in an inability to respond promptly to aerodynamic conditions after wind speed changes, leading to a significant deviation of the power trajectory from the normal optimized value, with some units experiencing a sustained power loss below the optimal level. Furthermore, FDIA—control signal attacks—directly cause the optimizer to obtain a forged "optimal control signal," leading to some units' pitch angle or tip speed ratio being guided to a suboptimal or even significantly deviated state from its optimal region, resulting in a more significant power loss than DoS attacks. FDIA—measured wind speed attacks, because they alter the input environment of the entire optimization process, cause the optimizer to operate under incorrect wind speed conditions. The generated control set is unlikely to achieve the expected aerodynamic efficiency under actual wind speeds, therefore its power loss is slightly lower than the normal optimization curve.

[0075] In summary, under normal circumstances, all indicators are in the high-efficiency operating range; DoS attacks cause the speed of rotation and tip speed ratio to lag with changes in wind speed, and the pitch angle deviates slightly; FDIA-measurement signals will cause some units to have systematically high or low aerodynamic variables; FDIA-control signals have the most serious impact, with the largest deviations in speed of rotation and tip speed ratio and significant fluctuations in pitch angle, resulting in very low overall operating power.

[0076] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0077] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for generating optimal attack strategies for wind farms, considering DoS and FDIA. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0078] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0079] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0080] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0081] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0082] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0083] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0084] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for generating an optimal attack strategy for wind farms considering DoS and FDIA, characterized in that, The method includes: Establish an optimized control model and an optimal scheduling model for normal operation of wind turbine units; FDIA control command tampering attack model, DoS attack model and FDIA wind speed measurement tampering attack model were constructed respectively. Combining wind turbine operation constraints and attack resource constraints, the system solves for the attack strategy that maximizes the total power output loss of the wind farm based on the constructed model, and completes a quantitative assessment of the attack impact.

2. The method for generating the optimal attack strategy for wind farms considering DoS and FDIA according to claim 1, characterized in that, The wind turbine optimization control model includes a wind turbine power output model, a power coefficient model, a tip speed ratio model, and constraints on wind speed, rotor speed, pitch angle, power coefficient, and tip speed ratio.

3. The method for generating the optimal attack strategy for wind farms considering DoS and FDIA according to claim 1, characterized in that, The optimal scheduling model for normal operation takes maximizing the total power of the wind farm as the optimization objective, solves for the optimal control parameters, and calculates the optimal power output.

4. The method for generating the optimal attack strategy for wind farms considering DoS and FDIA according to claim 1, characterized in that, The FDIA control command tampering attack model sets tampering constraints on the wind turbine angular velocity and pitch angle, and substitutes the tampered parameters into the power model to calculate the power output after the attack.

5. The method for generating the optimal attack strategy for wind farms considering DoS and FDIA according to claim 1, characterized in that, The DoS attack model freezes the wind turbine control variables to their values ​​before the attack and calculates the actual power output after the attack by combining them with the real-time wind speed.

6. The method for generating the optimal attack strategy for wind farms considering DoS and FDIA according to claim 1, characterized in that, The FDIA wind speed measurement tampering attack model injects forged wind speed data, solves for pseudo-optimal scheduling parameters based on the forged wind speed data, and calculates the actual power output by combining the real wind speed.

7. The method for generating the optimal attack strategy for wind farms considering DoS and FDIA according to claim 1, characterized in that, The quantitative assessment of the attack impact is based on normal optimized operation, comparing the deviations of indicators such as power output, rotational speed, tip speed ratio, and pitch angle.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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